Introduction to Built-in Algorithms
Based on the frequently-used AI engines in the industry, ModelArts provides built-in algorithms to meet a wide range of your requirements. You can directly select the algorithms for training jobs, without concerning model development.
Built-in algorithms of ModelArts adopt MXNet and TensorFlow engines and are mainly used for detection of object classes and locations, image classification, semantic image segmentation, and reinforcement learning.
Built-in algorithms on the Training Jobs > Built-in Algorithms page are algorithms of earlier versions, which will be brought offline soon. ModelArts has released more built-in algorithms in AI Gallery, which have higher precision and cover more scenarios.
Viewing Built-in Algorithms
In the left navigation pane of the ModelArts management console, choose Training Management > Training Jobs. On the displayed page, click Built-in Algorithms. In the built-in algorithm list, click 
next to an algorithm name to view details about the algorithm.
You can click Create Training Job in the Operation column for an algorithm to quickly create a training job, for which this algorithm serves as the Algorithm Source.
- Before using a built-in algorithm to create a training job, prepare and upload training data to OBS. For details about the data storage path and data format requirements, see Requirements on Datasets.
- The built-in algorithms hard_example_mining and feature_cluster are for internal use only and you cannot use them for training.
For details about the built-in algorithms and their running parameters, see the following:
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